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Application of Multivariate-Rank-Based Techniques in Clustering of Big Data

By: Material type: TextTextDescription: 179 -190 ppSubject(s): In: BANDOPADHYAY, TATHAGATA VIKALPA:THE JOURNAL OF DECISION MAKERSSummary: Very large or complex data sets, which are difficult to process or analyse using traditional data handling techniques, are usually referred to as big data. The idea of big data is characterized by the three ‘v’s which are volume, velocity, and variety (Liu, McGree, Ge, & Xie, 2015) referring respectively to the volume of data, the velocity at which the data are processed and the wide varieties in which big data are available. Every single day, different sectors such as credit risk management, healthcare, media, retail, retail banking, climate prediction, DNA analysis and, sports generate petabytes of data (1 petabyte = 250 bytes). Even basic handling of big data, therefore, poses significant challenges, one of them being organizing the data in such a way that it can give better insights into analysing and decision-making. With the explosion of data in our life, it has become very important to use statistical tools to analyse them.
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Holdings
Item type Current library Call number Vol info Status Notes Date due Barcode Item holds
Journal Article Journal Article Main Library Vol 43, Issue 4/ 5559951JA1 (Browse shelf(Opens below)) Available 5559951JA1
Journals and Periodicals Journals and Periodicals Main Library On Display JRNL/GEN/Vol 43, Issue 4/5559951 (Browse shelf(Opens below)) Vol 43, Issue 4 (10/10/2018) Not for loan October-December, 2018 5559951
Total holds: 0

Very large or complex data sets, which are difficult to process or analyse using traditional data handling techniques, are usually referred to as big data. The idea of big data is characterized by the three ‘v’s which are volume, velocity, and variety (Liu, McGree, Ge, & Xie, 2015) referring respectively to the volume of data, the velocity at which the data are processed and the wide varieties in which big data are available. Every single day, different sectors such as credit risk management, healthcare, media, retail, retail banking, climate prediction, DNA analysis and, sports generate petabytes of data (1 petabyte = 250 bytes). Even basic handling of big data, therefore, poses significant challenges, one of them being organizing the data in such a way that it can give better insights into analysing and decision-making. With the explosion of data in our life, it has become very important to use statistical tools to analyse them.

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